The invention relates to a method and a device for computer-based processing a template minutia set of a fingerprint and a computer readable storage medium.
In order to provide an identification mechanism based on biometric characteristics of a person which is to be identified, one biometric characteristic often used for personal verification/identification is the person's fingerprint.
In this kind of verification/identification, typically the person's fingerprint is detected by a fingerprint sensor, thereby generating a fingerprint image.
The word “fingerprint” is herein used as a representative of a fingerprint or a like pattern or figure. More particularly, the fingerprint may be an actual finger; a palm print, a toe print, a soleprint, a squamous pattern, and a streaked pattern composed of streaks. The fingerprint may also be a diagram drawn by a skilled person to represent a faint fingerprint remain which is, for example, left at the scene of a crime.
Usually, the person, who would like to use a device for this kind of verification/identification is required to register his or her fingerprint in a registration step for later verification/identification in a verification/identification step.
During the registration, characteristic features of the fingerprint will be extracted and stored in a storage media of the device. Such a fingerprint image is called the template fingerprint and such a stored characteristic features are called the template minutiae.
When a person wants to use the device, he has to present his fingerprint to the device.
The unknown fingerprint of the person who wants to be identified in a verification/identification step is usually called input fingerprint. The characteristic features of the input fingerprint will be extracted and matched against the template minutiae of the template fingerprint. If a match is found, the person is identified as the person the respective pre-stored template fingerprint refers to. Otherwise, the person is identified as an unauthorized user and the further use of the device will be prohibited.
The template minutiae usually comprise geometrical and other useful characteristic information (or features) pertaining to the local discontinuities (the minutiae) of the fingerprint, such as                the type of the current minutiae,        the location of the current minutiae,        the direction of the current minutiae,        the ridge count between the current minutiae and its neighboring minutiae,        the location of the neighboring minutiae,        the distance relationship of the current minutiae with respect to its neighboring minutiae, and/or        the angular relationship of the current minutiae with respect to its neighboring minutiae.        
In [1], [2] and [3], methods to determine the template minutiae are described. The basic concept of these methods is to determine the minutiae present in a single fingerprint image. From these determined minutiae, the required parameters are subsequently determined.
Furthermore, methods to match the fingerprints or to compare whether two fingerprints are similar to each other or not using the fingerprint templates, are described in [4], [5], [6] and [7].
Noise, contrast deficiency, improper image acquisition, geometrical transformation, deformation and skin elasticity will make a captured fingerprint image deviate from the ideal fingerprint of the person. The poor quality of the captured fingerprint image will make a reliable minutia extraction very difficult. Spurious minutiae can be produced, valid minutiae can be dropped and the minutia type (ending and bifurcation) may be exchanged due to the noise and interference of the fingerprint image. The problem of automatic minutia extraction has been thoroughly studied but never completely solved. The employment of various image enhancement techniques can only partly solve the problem [8]. In [2] five different minutia extraction techniques well known in the literature were implemented and their performances were compared. In the experiments disclosed in [2], the best technique dropped 4.51% genuine minutiae, produced 8.52% spurious minutiae and caused 13.03% minutiae exchanged their type. The total error is then 26.07%. For the rest four approaches, the total error is 33,83%, 119.80%, 207.52% and 216.79%, respectively. From these experimental results it is seen that a perfect minutia extraction is a very difficult task.
An automatic fingerprint verification/identification system will successively receive fingerprint images of users in the practical application after the registration. The fingerprint imaging condition, which causes the minutia extraction error, will change with time due to the change of the skin condition, climate and on-site environment. Thus, the image noises and interferences of fingerprints received by the system at different time might be quite different.
However, improving the fingerprint image quality based on these multiple images is very difficult and memory and computation too expensive due to the image size and image pose transformation.